Sequential Bayesian Polynomial Chaos Model Selection for Estimation of Sensitivity Indices

DOI10.1137/130931175zbMath1327.62443OpenAlexW1974459060MaRDI QIDQ2945143

Matthias Hwai Yong Tan

Publication date: 9 September 2015

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/130931175

zbMATH Keywords

orthonormal polynomialsBayesian model selectionfunctional ANOVA decompositiontotal sensitivity indexSobol index

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Design of statistical experiments (62K99)

Related Items (4)

ANOVA-MOP: ANOVA Decomposition for Multiobjective Optimization ⋮ A novel sparse polynomial chaos expansion technique with high adaptiveness for surrogate modelling ⋮ Sequential Design of Computer Experiments for the Solution of Bayesian Inverse Problems ⋮ Sequential Design of Computer Experiments for the Computation of Bayesian Model Evidence

Uses Software

• Matlab
• gss
• OPQ

Cites Work

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• Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach
• Variance-Based Global Sensitivity Analysis via Sparse-Grid Interpolation and Cubature
• Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates

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